We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. The sampled latent texture maps are then decoded into a final texture map. During the sampling process, we focus on both global and local consistency across multiple viewpoints: global consistency is achieved through the integration of style consistency mechanisms within the noise prediction network, and low-level consistency is achieved by dynamically aligning latent textures. Finally, we apply reference-based inpainting and img2img on denser views for texture refinement. Our approach overcomes the limitations of slow optimization in distillation-based methods and instability in inpainting-based methods. Experiments on meshes from various sources demonstrate that our method surpasses the baseline methods quantitatively and qualitatively.

中文翻译：

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GenesisTex：使图像去噪扩散适应纹理空间

我们提出了 GenesisTex，这是一种根据文本描述合成 3D 几何纹理的新颖方法。 GenesisTex 通过纹理空间采样将预训练的图像扩散模型适应纹理空间。具体来说，我们为每个视点维护一个潜在纹理图，该纹理图根据相应视点渲染时的预测噪声进行更新。然后将采样的潜在纹理图解码为最终纹理图。在采样过程中，我们关注多个视点的全局和局部一致性：全局一致性是通过在噪声预测网络中集成风格一致性机制来实现的，而低级一致性是通过动态对齐潜在纹理来实现的。最后，我们在更密集的视图上应用基于参考的修复和 img2img 以进行纹理细化。我们的方法克服了基于蒸馏的方法优化缓慢和基于修复的方法不稳定的局限性。对各种来源的网格进行的实验表明，我们的方法在数量和质量上都超越了基线方法。